Document Type
Article
Publication Date
2-7-2018
Abstract
Non-targeted analysis of environmental samples, using comprehensive two‐dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO® ChromaTOF® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post‐bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post‐bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO® ChromaTOF® software Statistical Compare feature. Based on this list, 63–100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold.
Recommended Citation
Titaley IA, Ogba OM, Chibwe L, Hoh E, Cheong PHY, Simonich SLM. Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry nontargeted analysis of comparative samples. J Chromatog A. 2018;1541:57–62. doi: 10.1016/j.chroma.2018.02.016
Copyright
Elsevier
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Environmental Chemistry Commons, Numerical Analysis and Scientific Computing Commons, Other Chemistry Commons, Other Computer Sciences Commons, Other Life Sciences Commons, Soil Science Commons
Comments
NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Chromatography A. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Chromatography A, volume 1541, in 2018. DOI: 10.1016/j.chroma.2018.02.016
The Creative Commons license below applies only to this version of the article.